Spiking neural network-based decision framework for real-time-ball-on-plate balancing: Sim-to-real
Spiking neural networks (SNNs), with temporal dynamics and biologically inspired spiking behavior, offer improved interpretability and performance in real-time control. This paper integrates reinforcement learning (RL) with reward-modulated, bio-inspired SNNs that employ reward-modulated spike-timing-dependent plasticity (R-STDP) to address real-time control tasks. The proposed model enables bio-inspired learning by combining conventional spike-timing-dependent plasticity (STDP) with time-dependent, reward-modulated weight updates. Furthermore, it reconstructs the task-oriented state space and reward mechanism to realize a cross-task control framework. Experimental results from control tasks, along with comparisons to traditional RL methods, demonstrate the effectiveness of SNNs in both simulation and real-world applications. This study aims to bridge the gap between SNNs and traditional feedback mechanisms in control systems, highlighting how bio-inspired approaches can enhance adaptive control.